Simulation of gait following controller of lower limb rehabilitation exoskeleton robot based on improved sliding mode algorithm

This study investigates the relationship between the effect of a rehabilitation exoskeleton and the trajectory tracking performance and stability of its controller through the simulation of the exoskeleton. Specifically, the stability and trajectory tracking performance of the RBF-neural sliding mode controller (RBF-SMC) were explored in this study. Firstly, the study develops a two-link model of the exoskeleton and solves the kinetic equations of the knee and hip joints using the Lagrange method. Secondly, the normal human gait curve is obtained through OpenSim software and fitted using a Fourier series to obtain the gait equation. In addition, the study introduces neural networks to approximate the modelling uncertainty terms in the control system, and develops a theoretical model of RBF-SMC. Stability analysis is carried out through Lyapunov stability theory. Finally, the study builds a simulation model of the motion control system using Matlab, and carries out trajectory tracking simulation experiments using human gait equations. The experimental results indicated the proposed RBF-SMC has better tracking performance than the (SMC) and can meet the needs of patients for passive rehabilitation training.